Introduction to CECL Quantification

Introduction to CECL Quantification

February 2017

2Introduction to CECL Quantification

Today’s Speakers

» Emil Lopez is a Director in the Enterprise Risk Solutions Group, based in New York, focusing on the

development of software and analytic solutions for impairment accounting (CECL/IFRS 9).

» Prior to joining the product strategy group, Mr. Lopez led risk rating and stress testing modeling projects

for Basel and DFAST institutions.

» Mr. Lopez received his MBA from New York University and received his BS in finance and business

administration from the University of Vermont.

» Ed Young is a Senior Director at Moody’s Analytics. He advises clients across the Americas on risk

management and regulatory expectations issues around capital planning, liquidity, and credit stress testing,

as well as allowance for credit loss processes.

» Prior to joining Moody’s Analytics, Ed spent ten years working for the Federal Reserve. During his tenure, he

participated on a multitude of Federal Reserve System initiatives related to capital planning, liquidity

planning, stress testing, credit risk management, interest rate risk management, and model risk

management.

Moderator

» Dr. Jing Zhang is Managing Director and the Global Head of Moody’s Analytics Research and

Modeling Group. His group is responsible for the quantitative modeling behind the EDF and LGD

models for both public and private firms, commercial real estate, portfolio and balance sheet, and

insurance analytics.

» PhD from the Wharton School of the University of Pennsylvania

» Editor of Risk Books “CCAR and Beyond,” and “From Incurred Loss to Expected Loss” (forthcoming)


3

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Welcome!

Moody's Analytics CECL Webinar Series:

Expected Credit Loss Quantification


Today

CRE CECL Methodologies

Tuesday, February 28, 2017 | 1:00PM EST

C&I CECL Methodologies

Tuesday, March 14, 2017 | 1:00PM EST

Retail CECL Methodologies


Structured Assets CECL Methodologies

Thursday, April 20, 2017 | 1:00PM EST

To find out more about Moody’s

Analytics perspectives on CECL

and register for our webinar series

visit:

www.moodysanalytics.com/cecl

http://www.moodysanalytics.com/cecl


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Topic 326: Measurement of Credit Losses on Financial Instruments; commonly known as “CECL”

Who/What does it apply to?

» All banks, savings associations, credit unions, and financial

institution holding companies, regardless of asset size

» Entities holding financial assets and net investment in leases

that are not accounted for at fair value through net income

» Includes: Loans, debt securities, trade receivables, net

investments in leases, off-balance-sheet credit exposures,

reinsurance receivables, etc.

» The standard requires organizations to immediately record the

full amount of credit losses that are expected over the lifetime

of the financial asset

When does it go into effect?

» FY 2019 (after 12/15/19) for public business entities that are

SEC filers

» FY 2020 (after 12/15/20) for all other public business entities

» FY 2021 (after 12/15/21) for all other entities,

» Early adoption permitted December 15, 2018

Held for Sale

Held for

Investment

Held to Maturity

Available for

Sale

Trading

New Impairment

Method

Other FVOCl/

FVPL Methods

Investment

Strategy

Impairment

Model

CECL

Lower of Am. Cost

Method/ Market

FV-NI

PCD

CECL

PCD

AFS Credit Loss

PCD

Loans/

Leases

Debt

Securities


In essence, the new standard is about improving the measurement of and reporting on credit losses

Institutions will need to measure and record immediately all expected credit losses (ECL) over

the life of their financial assets based on:

1) Past events, including historical experience

2) Current conditions

3) Reasonable and supportable forecasts

» Although “reasonable and supportable forecasts” are required, an entity will not need to create an

economic forecast over the entire contractual life of long-dated financial assets

» Institutions will have significant discretion over how they measure expected credit losses

» ECL recorded at origination and updated at subsequent reporting dates

If it effects the collectability of the reported amount, it should be considered!


Hypothetical illustration of the expected credit loss quantification process

Historical Experience

Unfavorable

Favorable

Deteriorating

Improving

Economic Forecast

31

Deteriorating

Improving

Qualitative Adjustment

ACL (Med-High)

ACL (High)

ACL (Very High)4

ACL (Medium)

ACL (Med-Low)

ACL (Low)

ACL (Very Low)

Level of the

Allowance for

Credit Losses

Example:

0.03%

Example:

3.00%

Current Conditions

2

NCOs

CECL

Term

Structure

Adjustment

ICLM

LEP

Adjustment

ICLM = Incurred Credit Loss Model (current GAAP) | CECL = Current Expected Credit Loss model (effective 2019-2021)

Impact on Allowance for Credit Losses (“ACL”)


Collective (“Pool”) Evaluation

» Required for financial assets when similar

risk characteristic(s) exists

Individual Evaluation

» Required when a financial asset does not

share risk characteristics with its other

financial assets

The ASU requires entities to apply one of two approaches to evaluate expected credit losses

» Internal or external credit score

» Risk ratings or classification

» Financial asset type

» Collateral type

» Size

Examples of Shared Risk Characteristics

» Effective interest rate

» Term

» Geographical location

» Industry of the borrower

» Vintage


Key Modeling Challenges for CECL

» Model methodology

– Loan-level versus cohort-level?

– Can we leverage existing stress testing, Basel or other models? How?

» Incorporating macroeconomic drivers in accounting:

– Which economic scenario? How many?

– Demonstrating relationship to credit losses

– Sensitivity analysis and scenario spreads

» Lifetime length determination: particularly for revolving products

– ECL for off balance sheet exposures to be reported separately

» Benchmarking to industry performance and previous crisis

– Support forecasts for audit

» Disclosures: models, production processes and reporting platforms need to

support required reporting


1. What is the most significant challenge you anticipate in CECL implementation?

A. Data availability for ECL modeling

B. ECL quantification

C. Scenario design

D. Qualitative/Management Overlay methodology

E. Performance (i.e., speed of execution)

F. Data and processes governance/controls


0%

5%

10%

15%

20%

25%

30%

35%

40%

What is the most significant challenge you anticipate in CECL implementation?


CECL Calculation: Strategic and Tactical Considerations

Strategic

Considerations

Criteria

Technical

Considerations

» Incorporate historical experience

» Incorporate current conditions

» Incorporate forward-looking information

» Forecast life of loan ECL

» Segment and granularity appropriate

» Portfolio materiality

» Data availability: historical and reporting-date data

» Development costs: short-term vs. long-term investments

» Timing

» Invest in data, measurement and system capabilities for both CECL

and other applications

» Consider the impact of less granular quantification on

competitiveness

» Consider the impacts on lending and other business decisions

» Coordination and alignment with other processes

» Interactions with various internal and external stakeholders

One Size Does Not Fit All!


Coordination and Alignment With Other Processes---And Considering the interactions with various stakeholders

A

Origination, relationship

management, Portfolio

management

Planning and Budgeting

Risk Management

CCAR/DFASTCECLFinance and Treasury

ALM

Loan Pricing

Scenario

Forecast

Bank

Regulators

Auditor and

external market

Customers


Possible Methodologies

» Loss rate methods

– Average charge-off method

– Static pool analysis

– Vintage analysis

» PD/LGD rating method

– Basel models

– Internal rating models

– Granular stress testing models

» Roll-rate method (migration analysis)

» Discounted cash flow analysis

Both statistical and qualitative analysis can be applied to any method to

» Incorporate forward-looking assessment (e.g. linking loss rate to forecast)

» Account for life-time loss

For most banks, the

typical approach is to

leverage or build on top

of existing approaches


2. What is/are the approach(es) you use in ALLL estimation today for your commercial portfolios?

A. Loss rates based on peer institution data (e.g. call report data)

B. Loss rates based on internal experience (e.g. static pool analysis)

C. Rating migration / roll rate

D. Vintage analysis

E. Dual risk ratings (i.e., PD / LGD)

F. Other


0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Loss rates basedon peer institution

data (e.g. call reportdata)

Loss rates basedon internal

experience (e.g.static pool analysis)

Rating migration /roll rate

Vintage analysis Dual risk ratings(i.e., PD / LGD)

Other No Answer

What is the approach you use in ALLL estimation today for your commercial portfolios?


3. What is/are the approach(es) you use in ALLL estimation today for your retail portfolios?

A. Loss rates based on peer institution data (e.g. call report data)

B. Loss rates based on internal experience (e.g. static pool analysis)

C. Rating migration / roll rate

D. Vintage analysis

E. Dual risk ratings (i.e., PD / LGD)

F. Other


0%

10%

20%

30%

40%

50%

60%

Loss rates basedon peer institution

data (e.g. call reportdata)

Loss rates basedon internal

experience (e.g.static pool analysis)

Rating migration /roll rate

Vintage analysis Dual risk ratings(i.e., PD / LGD)

Other No Answer

What is the approach you use in ALLL estimation today for your retail portfolios?


Building Upon the Loss Rate Approach

Starting Point

» Apply a historic loss

rate percentage, either

collective or individual

evaluation

» Average charge-off

method

» Static pool analysis

» Vintage analysis

Enhancement

† Incorporate forward-

looking assessment

† Link loss rate directly

or indirectly to macro

forecast

† Extend annual loss to

life-time loss

† Leverage external

data if appropriate

Considerations

Easy to implement

Close to the current

practice

“Q” factor can be used

to incorporate forward-

looking assessment

Proper segmentation

and granularity is

important

Considering the

linkage with business

decisions such as loan

underwriting


Leveraging the Roll Rate Approach

Starting Point

» Compute percentages

of assets that will “roll”

or “migrate” to a more

severe risk rating or

delinquency status.

» Roll-rate percentages

are applied to the

balance in each

category to estimate

the amount that will

migrate to the next

category.

» Aggregate total

migration for each

category to determine

the allowance.

Enhancement

† Incorporate forward-

looking assessment

† Linking the “roll” or

“migration” to

macroeconomic

forecast

† Incorporate life-time

loss

Considerations

Linking credit

migration to

macroeconomic

forecast: statistically

or qualitatively?

Consider the

linkage with

business decisions

such as setting loan

terms


Leveraging the Dual Rating Framework

Starting Point

» The PD/LGD/EAD

framework for Basel

IRB PD

» The dual rating

framework currently

used in business

decision such as loan

pricing, limit setting, risk

monitoring and setting

reserve

Enhancement

† Adjust “regulatory”

definition to

“accounting”

definition

† TTC to PIT

conversion to

incorporate forward

looking assessment

† Extend term structure

of PD and LGD

Considerations

Easier to implement

for Basel IRB banks

(this is the most

popular approach

for IFRS 9

implementation)

There is a wealth of

data and expertise

in place as a result

of IRB compliance

Easier to assess

impact on business

decisions such as

setting loan term

pricing and terms


PD, LGD, EAD Modeling Under Basel and CECL

BASEL CECL

PD 1. 1-year TTC (through-the-cycle) PD based on

historical long run average default rate;

2. Subject to prescribed regulatory floors (e.g.

3.b.p for some portfolio)

3. Estimated based on minimum five years of

historical data

1. Term structure of PDs

2. Point-in-time (PIT) PD measures;

3. Include historical, current and forward looking

elements at reporting date for all possible

outcomes;

4. No prescribed regulatory floors

LGD 1. “Downturn” LGD to reflect adverse economic

scenarios.

2. Consider both direct and indirect costs

associated with collection of the exposure;

3. Regulatory prescribed floors;

4. Discount rate based on weighted average cost

of capital or risk-free rate;

5. Min. 5 year data for retail and 7 year for

sovereign, corporate and bank exposures

1. “Current ” or “forward looking” to reflect impact of

economic scenarios;

2. Only costs directly attributable to the collection of

recoveries (remove the collective cost in BASEL

LGD);

3. Discount rate based effective interest rate;

4. Timely evaluations of collateral value and

consideration of future value changes

EAD 1. “Downturn” EAD to reflect what would be

expected during a period of economic downturn

conditions;

2. Estimates take into consideration of on/off

balance sheet exposures adjusted for

estimated future draw downs over the lifetime.

1. Consider all contractual terms (e.g. prepayment,

usage, call and similar options) over the lifetime;

2. Adjust for firm’s estimates for the undrawn

commitments


Starting Point

» CCAR/DFAST or other

stress testing models

» These models have

gone through extensive

and rigorous validation

and benchmark

» Resource and expertise

built for CCAR/DFAST

Enhancement

† Calibrate the models to

both “adverse/stress”

and “normal” scenarios

† Extend term structure

of PD and LGD

† Enhance segmentation

and granularity

Considerations

More seamless

integration with

CCAR/DFAST

process

There is a wealth of

data and expertise

already in place as a

result of CCAR

implementation

Consider the linkage

with other business

applications such as

underwriting, pricing

of loans etc.

Leveraging the CCAR/DFAST Stress Testing Framework


4. How do you plan to incorporate forward-looking information to expected credit loss estimates?

A. Modeled: Linking macroeconomic variables to loss estimates quantitatively

B. Overlay: adding impact of macroeconomic forecast to the expected credit loss through

more qualitative measures

C. Mix of quantitative models and qualitative overlays

D. Have not decided


0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Modeled: Linkingmacroeconomic variables toloss estimates quantitatively

Overlay: adding impact ofmacroeconomic forecast to

the expected credit lossthrough more qualitative

measures

Mix of quantitative modelsand qualitative overlays

Have not decided No Answer

How do you plan to incorporate forward-looking information to expected credit loss estimates?


Business Impacts of CECL can be Significant and Profound

Loss allowance under CECL and FAS 5&114 can

differ materially due to differences

» between PIT and TTC PDs

» lifetime expected loss and the assumed loss

emergence period

CECL may affect

» Fluctuation / volatility in provision and earnings,

» Available capital

These impacts on earnings and capital may be

significant with consequences for

» Loan origination---loan product term and pricing

» Customer relationship

» Hedging and other credit portfolio decisions

Will my approaches to estimating CECL help track and monitor these impacts? Will they

help design metrics to reduce potential volatility—e.g. by diversifying loans within portfolios

to reduce concentrations of credit risk or adjusting maturities of loans?


Key Takeaways

» Modeling expected credit losses represents one of they key challenges of the

CECL accounting standard

» Existing risk measurement tools used for regulatory capital, internal ratings,

and stress testing can serve as good starting points. However, necessary

enhancements may include:

Segmentation granularity

Adjustment for current conditions

Linkage to forward-looking scenarios

Extension to life of loan

Adjustment for built-in bias (i.e. conservatism)

» The appropriate approach ultimately depends on portfolio- and organization-

specific considerations, including data availability and interconnectedness of

banking functions

» The rest of the webinar series will focus on modeling considerations for

specific asset classes

Welcome!

Moody's Analytics CECL Webinar Series:

Expected Credit Loss Quantification


Today

CRE CECL Methodologies

Tuesday, February 28, 2017 | 1:00PM EST

C&I CECL Methodologies


Retail CECL Methodologies


Structured Assets CECL Methodologies

Thursday, April 20, 2017 | 1:00PM EST

To find out more about Moody’s

Analytics perspectives on CECL

and register for our webinar series

visit:

www.moodysanalytics.com/cecl

http://www.moodysanalytics.com/cecl


Moody’s Credit Loss and Impairment Analysis Suite

Modeling & Advisory Services

Economic

Scenarios

Credit Data

Credit Modeling &

EL Calculation

Workflow and Automation

Qualitative Overlay

Management

Analysis & Reporting

Solutions to Support CECL Impairment Calculation


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Introduction to CECL Quantification

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